Efficient analysis of Defects using Naive Bayes Classification and Impact on Effort and Cost of the Project

摘要

Research work focuses on impact of defects identified in various stages of Software Development Life Cycle (SDLC) on overall project effort and cost. Naive Bayes classification technique is used to identify the bug tracking, discovery and maintenance of risky modules. Statistical analysis is performed for analyzing the impact on effort and cost of a project. In this paper, bug tracking is represented as a variable of severity which can be analyzed based on classification and can be displayed high posterior in plot on defect stage in each phase.